What question did this study set out to answer?

To enhance understanding of tourist travel intentions and develop effective route planning methods.

A general-purpose tourism intent understanding and route planning based on multimodal spatio-temporal pre-training

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To enhance understanding of tourist travel intentions and develop effective route planning methods.
Developed a multi-modal model using timestamp-aligned heterogeneous graphs.
Implemented graph convolution networks for better semantic interaction.
Introduced a dynamic weight mechanism based on Pearson correlation for feature fusion.
Designed a multi-objective route planning method combining heuristic search and evolutionary strategies.
Achieved recognition accuracy of 93.51% for travel intention identification.
Generated optimal route with a reverse generation distance of only 1.9.
Demonstrated strong generalization and practical value in tourism applications.

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This research demonstrates improved route planning in tourism, suggesting enhanced travel experience by understanding intentions.

Qiong Zhang (2026) studied this question.

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